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Indoor Localization Systems: Latest Advances and Prospects

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 48745

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Special Issue Editors

Dr. Francesco Guidi

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Guest Editor

WiLAB, CNIT, IEIIT, CNR, Viale Risorgimento 2, 40136 Bologna, Italy
Interests: positioning; UWB; millimeter-wave; RFID; radar
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Dr. Nicolò Decarli

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Guest Editor

University of Bologna, Italy
Interests: localization; signal processing; UWB radio; RFID; low-range radar

Dr. Davide Dardari

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Guest Editor

Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Viale dell’Università 50, 47522 Cesena, Italy
Interests: wireless communications; localization; distributed signal processing; RFID
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor localization has attracted a great research interest in the last few years, thanks to the possibility of exploiting many different technologies, spanning from radio (e.g., WiFi, Bluetooth and, more recently, UWB) to inertial sensors and laser/camera-based systems, which have been proven to be effective in GPS-denied scenarios. Most of these solutions are currently turning into end-products, while new technologies, such as millimeter-wave massive arrays, visible light, and monocular cameras, are approaching the panorama, opening new opportunities and also challenging problems.

Based on this perspective, this Special Issue aims to bring together the recent developments in indoor localization and to address new advancements beyond Internet of Things and 5G scenarios. Submissions are expected either to focus on new theoretical aspects, practical schemes, experimentation or to review a recent trend in one of the diverse aspects of localization, discussing today’s challenges and addressing possible future directions.

Potential topics include but are not limited to:

Localization and tracking for 5G and IoT
Simultaneous localization and mapping (SLAM)
Multisensors localization and data fusion
Deep learning for positioning
Fundamental limits
Position-dependent parameter estimation
Fingerprinting and map matching algorithms
Localization and tracking via signals of opportunity
Machine learning and crowdsensing enable localization
UWB and millimeter-wave technologies
Energy efficient positioning systems
Low-range radar and RFID
Vision and visible light-based localization
Secure localization and privacy
Testbeds and experimentation

Dr. Francesco Guidi
Dr. Nicolò Decarli
Prof. Davide Dardari
Guest Editors

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Published Papers (9 papers)

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Research

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24 pages, 23670 KiB

Open AccessArticle

Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment

by Yu Meng, Kwei-Jay Lin, Bo-Lung Tsai, Ching-Chi Chuang, Yuheng Cao and Bin Zhang

Appl. Sci. 2020, 10(23), 8583; https://doi.org/10.3390/app10238583 - 30 Nov 2020

Cited by 3 | Viewed by 2600

Abstract

Localization is an important technology for smart services like autonomous surveillance, disinfection or delivery robots in future distributed indoor IoT applications. Visual-based localization (VBL) is a promising self-localization approach that identifies a robot’s location in an indoor or underground 3D space by using its camera to scan and match the robot’s surrounding objects and scenes. In this study, we present a pictorial planar surface based 3D object localization framework. We have designed two object detection methods for localization, ArPico and PicPose. ArPico detects and recognizes framed pictures by converting them into binary marker codes for matching with known codes in the library. It then uses the corner points on a picture’s border to identify the camera’s pose in the 3D space. PicPose detects the pictorial planar surface of an object in a camera view and produces the pose output by matching the feature points in the view with that in the original picture and producing the homography to map the object’s actual location in the 3D real world map. We have built an autonomous moving robot that can self-localize itself using its on-board camera and the PicPose technology. The experiment study shows that our localization methods are practical, have very good accuracy, and can be used for real time robot navigation. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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23 pages, 1555 KiB

Open AccessArticle

UWB Indoor Localization Using Deep Learning LSTM Networks

by Alwin Poulose and Dong Seog Han

Appl. Sci. 2020, 10(18), 6290; https://doi.org/10.3390/app10186290 - 10 Sep 2020

Cited by 158 | Viewed by 11229

Abstract

Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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20 pages, 3067 KiB

Open AccessArticle

Gaussian Process Modeling of Specular Multipath Components

by Anh Hong Nguyen, Michael Rath, Erik Leitinger, Khang Van Nguyen and Klaus Witrisal

Appl. Sci. 2020, 10(15), 5216; https://doi.org/10.3390/app10155216 - 29 Jul 2020

Cited by 5 | Viewed by 2378

Abstract

The consideration of ultra-wideband (UWB) and mm-wave signals allows for a channel description decomposed into specular multipath components (SMCs) and dense/diffuse multipath. In this paper, the amplitude and phase of SMCs are studied. Gaussian Process regression (GPR) is used as a tool to analyze and predict the SMC amplitudes and phases based on a measured training data set. In this regard, the dependency of the amplitude (and phase) on the angle-of-arrival/angle-of-departure of a multipath component is analyzed, which accounts for the incident angle and incident position of the signal at a reflecting surface—and thus for the reflection characteristics of the building material—and for the antenna gain patterns. The GPR model describes the similarities between different data points. Based on its model parameters and the training data, the amplitudes of SMCs are predicted at receiver positions that have not been measured in the experiment. The method can be used to predict a UWB channel impulse response at an arbitrary position in the environment. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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23 pages, 6154 KiB

Open AccessArticle

A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System

by Zhongliang Deng, Buyun Jia, Shihao Tang and Xiao Fu

Appl. Sci. 2020, 10(10), 3434; https://doi.org/10.3390/app10103434 - 15 May 2020

Cited by 1 | Viewed by 2617

Abstract

A novel communication and navigation fusion system (CNFS) based on cellular communication system is developed to realize high-accuracy localization. Because of the small signal coverage of every transmitter and the fluctuation of received signal and noise, the CNFS positioning receiver requires fast processing of signal and stable performance. As the first operation performed by receiver, signal acquisition with fast speed and stable detection performance should be realized. This paper proposed a novel fast acquisition method with fine detection performance using search space reduction (SSR) and DD-MAX/TC-CACFAR techniques. SSR method is utilized to reduce the size and number of search space in the cross ambiguity function (CAF) evaluation stage. DD-MAX/TC-CACFAR method is employed to realize fine and stable detection performance in signal detection stage. The expressions of acquisition time are then derived considering the size and number of search space and the detection performance. Theoretical performance analysis and Monte Carlo simulation, which contain the comparison with other acquisition method, are presented to demonstrate the effectiveness of proposed method. Simulation and analysis results show that the proposed method can realize fast acquisition with fine and stable detection performance. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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17 pages, 1215 KiB

Open AccessArticle

An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems

by Tianyu Wang, Hanying Zhao and Yuan Shen

Appl. Sci. 2020, 10(1), 57; https://doi.org/10.3390/app10010057 - 19 Dec 2019

Cited by 39 | Viewed by 5179

Abstract

Ultra-wideband technology has the merits of high temporal resolution and stability, and it has been widely used for high-accuracy localization and tracking. However, most ultra-wideband localization systems need multiple anchors for trilateration, which results in high system cost, large messages overhead, and insufficient extraction of information. In this paper, we propose a single-anchor localization (SAL) mehtod that achieves high-accuracy multi-agent localization with high efficiency. In the proposed method, the anchor broadcasts the first two messages and then each agent responds one message to the anchor (quasi-)simultaneously. Based on the received message with superpositioned agent responses, the time-of-flight and angle-of-arrival information from all agents to the anchor can be extracted altogether. We implement the localization system in two indoor environments, and show that the proposed method can achieve decimeter-level accuracy for multiple agents using three messages. Our method provides design guidelines for high-accuracy and high-efficiency multi-agent localization systems. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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27 pages, 579 KiB

Open AccessArticle

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

by Zain Ul Abiden Akhtar and Hongyu Wang

Appl. Sci. 2019, 9(24), 5268; https://doi.org/10.3390/app9245268 - 4 Dec 2019

Cited by 12 | Viewed by 3019

Abstract

In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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17 pages, 4716 KiB

Open AccessArticle

A Novel Localization Technique Using Luminous Flux

by Muhammad Irshad, Wenyuan Liu, Jehangir Arshad, M. Noman Sohail, Aparna Murthy, Maryam Khokhar and M Musa Uba

Appl. Sci. 2019, 9(23), 5027; https://doi.org/10.3390/app9235027 - 21 Nov 2019

Cited by 24 | Viewed by 3282

Abstract

As global navigation satellite system (GNNS) signals are unable to enter indoor spaces, substitute methods such as indoor localization-based visible light communication (VLC) are gaining the attention of researchers. In this paper, the systematic investigation of a VLC channel is performed for both direct and indirect line of sight (LoS) by utilizing the impulse response of indoor optical wireless channels. In order to examine the localization scenario, two light-emitting diode (LED) grid patterns are used. The received signal strength (RSS) is observed based on the positional dilution of precision (PDoP), a subset of the dilution of precision (DoP) used in global navigation satellite system (GNSS) positioning. In total, 31 × 31 possible positional tags are set for a given PDoP configuration. The values for positional error in terms of root mean square error (RMSE) and the sum of squared errors (SSE) are taken into consideration. The performance of the proposed approach is validated by simulation results according to the selected indoor space. The results show that the position accuracy enhanced is at short range by 24% by utilizing the PDoP metric. As confirmation, the modeled accuracy is compared with perceived accuracy results. This study determines the application and design of future optical wireless systems specifically for indoor localization. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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16 pages, 2505 KiB

Open AccessArticle

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

by Ahmet Çağdaş Seçkin and Aysun Coşkun

Appl. Sci. 2019, 9(18), 3665; https://doi.org/10.3390/app9183665 - 4 Sep 2019

Cited by 20 | Viewed by 3940

Abstract

Wi-Fi-based indoor positioning offers significant opportunities for numerous applications. Examining the Wi-Fi positioning systems, it was observed that hundreds of variables were used even when variable reduction was applied. This reveals a structure that is difficult to repeat and is far from producing a common solution for real-life applications. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. To that end, machine learning (ML) methods are compared and the results of successful methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from the dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. It suggests that the proposed method results in a significant improvement for the estimation of floor and longitude. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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Review

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44 pages, 3092 KiB

Open AccessReview

Review of Indoor Positioning: Radio Wave Technology

by Tan Kim Geok, Khaing Zar Aung, Moe Sandar Aung, Min Thu Soe, Azlan Abdaziz, Chia Pao Liew, Ferdous Hossain, Chih P. Tso and Wong Hin Yong

Appl. Sci. 2021, 11(1), 279; https://doi.org/10.3390/app11010279 - 30 Dec 2020

Cited by 118 | Viewed by 13882

Abstract

The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations. Full article

(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)

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